R package provides a computationally efficient implementation of the highly adaptive lasso, a flexible nonparametric regression and machine learning algorithm endowed with several theoretically convenient properties.
hal9001 pairs an implementation of this estimator with an array of practical variable selection tools and sensible defaults in order to improve the scalability of the algorithm. By building on existing
R packages for lasso regression and leveraging compiled code in key internal functions, the
R package provides a family of highly adaptive lasso estimators suitable for use in both modern large-scale data analysis and cutting-edge research efforts at the intersection of statistics and machine learning, including the emerging subfield of computational causal inference.